BACKGROUND: Long noncoding RNAs (lncRNAs) are noncoding RNA transcripts greater than 200 nucleotides in length and are known to play a role in regulating the transcription of genes involved in vital cellular functions. We hypothesized the disease process in dysferlinopathy is linked to an aberrant expression of lncRNAs and messenger RNAs (mRNAs).
OBJECTIVE: In this study, we compared the lncRNA and mRNA expression profiles between wild-type and dysferlin-deficient murine myoblasts (C2C12 cells).
METHODS: LncRNA and mRNA expression profiling were performed using a microarray. Several lncRNAs with differential expression were validated using quantitative real-time polymerase chain reaction. Gene Ontology (GO) analysis was performed to understand the functional role of the differentially expressed mRNAs. Further bioinformatics analysis was used to explore the potential function, lncRNA-mRNA correlation, and potential targets of the differentially expressed lncRNAs.
RESULTS: We found 3195 lncRNAs and 1966 mRNAs that were differentially expressed. The chromosomal distribution of the differentially expressed lncRNAs and mRNAs was unequal, with chromosome 2 having the highest number of lncRNAs and chromosome 7 having the highest number of mRNAs that were differentially expressed. Pathway analysis of the differentially expressed genes indicated the involvement of several signaling pathways including PI3K-Akt, Hippo, and pathways regulating the pluripotency of stem cells. The differentially expressed genes were also enriched for the GO terms, developmental process and muscle system process. Network analysis identified 8 statistically significant (P<.05) network objects from the upregulated lncRNAs and 3 statistically significant network objects from the downregulated lncRNAs.
CONCLUSIONS: Our results thus far imply that dysferlinopathy is associated with an aberrant expression of multiple lncRNAs, many of which may have a specific function in the disease process. GO terms and network analysis suggest a muscle-specific role for these lncRNAs. To elucidate the specific roles of these abnormally expressed noncoding RNAs, further studies engineering their expression are required.

Primary liver cancer is the second leading cause of cancer-related death worldwide. At present, liver cancer is often in an advanced stage once diagnosed, and treatment effects are generally poor. Therefore, there is an urgent need for other powerful treatments. Macrophages are an important component of the tumor microenvironment, and macrophage polarization is crucial to tumor proliferation and differentiation. Regulatory interactions between macrophage subtypes, such as M1 and M2, lead to a number of clinical outcomes, including tumor progression and metastasis. So, it is important to study the drivers of this process. Long non-coding RNA has been widely proven to be of great value in the early diagnosis and treatment of tumors. Many studies have shown that long non-coding RNA participates in macrophage polarization through its ability to drive M1 or M2 polarization, thereby participating in the occurrence and development of liver cancer. In this article, we systematically elaborated on the long non-coding RNAs involved in the polarization of liver cancer macrophages, hoping to provide a new idea for the early diagnosis and treatment of liver cancer. Liver cancer- related studies were retrieved from PubMed. Based on our identification of LncRNA and macrophage polarization as powerful therapies for liver cancer, we analyzed research articles in the PubMed system in the last ten years on the crosstalk between LncRNA and macrophage polarization. By targeting M1/M2 macrophage polarization, LncRNA may promote or suppress liver cancer, and the references are determined primarily by the article\'s impact factor. Consequently, the specific mechanism of action between LncRNA and M1/M2 macrophage polarization was explored, along with the role of their crosstalk in the occurrence, proliferation, and metastasis of liver cancer. lncRNA is bidirectionally expressed in liver cancer and can target macrophage polarization to regulate tumor behavior. lncRNA mainly functions as ceRNA and can participate in the crosstalk between liver cancer cells and macrophages through extracellular vesicles. lncRNA can potentially participate in the immunotherapy of liver cancer by targeting macrophages and becoming a new biomolecular marker of liver cancer.

This commentary explores the burgeoning field of disulfidptosis-related long noncoding RNAs (lncRNAs) in the prognosis and therapeutic targeting of colorectal cancer (CRC). By evaluating recent research, including the pivotal study \"Predicting colorectal cancer prognosis based on long noncoding RNAs of disulfidptosis genes\" by Wang et al, this analysis underscores the critical role of lncRNAs in deciphering the molecular complexities of CRC. Highlighting the innovative methodologies and significant findings, I discuss the implications for patient survival, therapeutic response, and the potential of lncRNAs as biomarkers for precision medicine. The integration of bioinformatics, clinical databases, and molecular biology in these studies offers a promising avenue for advancing CRC treatment strategies and improving patient outcomes.

RNA biology has revolutionized cancer understanding and treatment, especially in endocrine-related malignancies. This editorial highlights RNA\'s crucial role in cancer progression, emphasizing its influence on tumor heterogeneity and behavior. Processes like alternative splicing and noncoding RNA regulation shape cancer biology, with microRNAs, long noncoding RNAs, and circular RNAs orchestrating gene expression dynamics. Aberrant RNA signatures hold promise as diagnostic and prognostic biomarkers in endocrine-related cancers. Recent findings, such as aberrant PI3Kδ splice isoforms and epithelial-mesenchymal transition-related lncRNA signatures, unveil potential therapeutic targets for personalized treatments. Insights into m6A-associated lncRNA prognostic models and the function of lncRNA LINC00659 in gastric cancer represents ongoing research in this field. As understanding of RNA\'s role in cancer expands, personalized therapies offer transformative potential in managing endocrine-related malignancies. This signifies a significant stride towards precision oncology, fostering innovation for more effective cancer care.

In eukaryotes, the Suv39 family of proteins tri-methylate lysine 9 of histone H3 (H3K9me) to form constitutive heterochromatin. However, how Suv39 proteins are nucleated at heterochromatin is not fully described. In the fission yeast, current models posit that Argonaute1-associated small RNAs (sRNAs) nucleate the sole H3K9 methyltransferase, Clr4/SUV39H, to centromeres. Here, we show that in the absence of all sRNAs and H3K9me, the Mtl1 and Red1 core (MTREC)/PAXT complex nucleates Clr4/SUV39H at a heterochromatic long noncoding RNA (lncRNA) at which the two H3K9 deacetylases, Sir2 and Clr3, also accumulate by distinct mechanisms. Iterative cycles of H3K9 deacetylation and methylation spread Clr4/SUV39H from the nucleation center in an sRNA-independent manner, generating a basal H3K9me state. This is acted upon by the RNAi machinery to augment and amplify the Clr4/H3K9me signal at centromeres to establish heterochromatin. Overall, our data reveal that lncRNAs and RNA quality control factors can nucleate heterochromatin and function as epigenetic silencers in eukaryotes.

This study aims to elucidate the key regulatory molecules, specifically messenger RNAs (mRNAs), long noncoding RNAs (lncRNAs), and microRNAs (miRNAs) and their roles in the development and progression of spinal cord injury (SCI). Expression profiles (GSE45006, GSE19890, and GSE125630) for SCI were sourced from the Gene Expression Omnibus (GEO) database. By comparing rats with SCI at various time points against those without SCI, we identified differentially expressed mRNAs (DEmRNAs), lncRNAs (DElncRNAs), and miRNAs (DEmiRNAs). The GSE45006 dataset facilitated the production of DEmRNAs, which were then clustered using Mfuzz. Subsequently, we constructed a protein-protein interaction (PPI) network and anticipated interaction pairs between miRNA-mRNA and lncRNA-mRNA. These pairs were instrumental in forming a regulatory network involving lncRNA-miRNA-mRNA interactions. Additionally, we conducted functional enrichment studies on the DEmRNAs within these gene networks. A total of 2313 DEmRNAs were identified using the GSE45006 dataset, alongside 111 DEmiRNAs from GSE19890. From GSE125630, we extracted 154 DElncRNAs and 2322 DEmRNAs. Our analysis revealed 294 up-regulated DEmRNAs, grouped into the up-cluster, and 407 down-regulated DEmRNAs, forming the down-cluster. Key hub genes in the PPI network, such as Rhof, Vav1, Lyz2, Rab3a, Lyn, Cyfip1, Gns, and Nckap1l, were identified. Additionally, the study successfully constructed a competing endogenous RNA (ceRNA) network, revealing 55 unique lncRNA-miRNA-mRNA link pairs. Our research established a ceRNA network associated with SCI, identifying several critical lncRNA-miRNA-mRNA connection pairs integral to the disease\'s onset and progression. Notably, significant associations, including the AABR07041411.1-miR-125a-5p-Slc4a7 and the Smg1-rno-miR-331-3p-Tlr4 pairs, were observed to exert a significant influence within this biological context.

Long noncoding RNAs (lncRNAs) have been demonstrated to be involved in biological processes, both physiological and pathological, including cancer, cardiovascular diseases, multiple sclerosis, autoimmune hepatitis and types I and II diabetes. LncRNAs are also known to have a critical role in the physiology of skin, and in the pathology of cutaneous diseases. LncRNAs are involved in a wide range of biological activities, including transcriptional post‑transcriptional processes, epigenetics, RNA splicing, gene activation and or silencing, modifications and/or editing; therefore, lncRNAs may be useful as potential targets for disease treatment. Hidradenitis suppurativa (HS), also termed acne inversa, is a major skin disease, being an inflammatory disorder that affects ~1% of global population in a chronic manner. Its pathogenesis, however, is only partly understood, although immune dysregulation is known to have an important role. To investigate the biological relevance of lncRNAs with HS, the most differentially expressed lncRNAs and mRNAs were first compared. Furthermore, the lncRNA‑microRNA regulatory network was also defined via reverse transcription‑quantitative PCR analysis, whereby a trio of lncRNA expression signatures, lncRNA‑TINCR, lncRNA‑RBM5‑ASI1 and lncRNA‑MRPL23‑AS1, were found to be significantly overexpressed in patients with HS compared with healthy controls. In conclusion, the three lncRNAs isolated in the present study may be useful for improving the prognostic prediction of HS, as well as contributing towards an improved understanding of the underlying pathogenic mechanisms, thereby potentially providing new therapeutic targets.

Long noncoding RNAs (LncRNAs) are key regulators of gene expression and can mediate their effects in both the nucleus and cytoplasm. Some of the best-characterized lncRNAs are localized within the nucleus, where they modulate the nuclear architecture and influence gene expression. In this review, we discuss the role of lncRNAs in nuclear architecture in the context of their gene regulatory functions in innate immunity. Here, we discuss various approaches to functionally characterize nuclear-localized lncRNAs and the challenges faced in the field.

BACKGROUND: Necroptosis-related long noncoding RNAs (lncRNAs) play crucial roles in cancer initiation and progression. Nevertheless, the role and mechanism of necroptosis-related lncRNAs in soft tissue sarcomas (STS) is so far unknown and needs to be explored further.
METHODS: Clinical and genomic data were obtained from the UCSC Xena database. All STS patients\' subclusters were performed by unsupervised consensus clustering method based on the prognosis-specific lncRNAs, and then assessed their survival advantage and immune infiltrates. In addition, we explored the pathways and biological processes in subclusters through gene set enrichment analysis. At last, we established the necroptosis-related lncRNA-based risk signature (NRLncSig) using the least absolute shrinkage and selection operator (LASSO) method, and explored the prediction performance and immune microenvironment of this signature in STS.
RESULTS: A total of 911 normal soft tissue samples and 259 STS patients were included in current study. 39 prognosis-specific necroptosis-related lncRNAs were selected. Cluster 2 had a worse survival than the cluster 1 and characterized by different immune landscape in STS. A worse outcome in the high-risk group was observed by survival analysis and indicated an immunosuppressive microenvironment. The ROC curve analyses illustrated that the NRLncSig performing competitively in prediction of prognosis for STS patients. In addition, the nomogram presents excellent performance in predicting prognosis, which may be more beneficial towards STS patients\' treatment.
CONCLUSIONS: Our result indicated that the NRLncSig could be a good independent predictor of prognosis, and significantly connected with immune microenvironment, thereby providing new insights into the roles of necroptosis-related lncRNAs in STS.

Long noncoding RNAs (lncRNAs) have been discovered to be extensively involved in eukaryotic epigenetic, transcriptional, and post-transcriptional regulatory processes with the advancements in sequencing technology and genomics research. Therefore, they play crucial roles in the body\'s normal physiology and various disease outcomes. Presently, numerous unknown lncRNA sequencing data require exploration. Establishing deep learning-based prediction models for lncRNAs provides valuable insights for researchers, substantially reducing time and costs associated with trial and error and facilitating the disease-relevant lncRNA identification for prognosis analysis and targeted drug development as the era of artificial intelligence progresses. However, most lncRNA-related researchers lack awareness of the latest advancements in deep learning models and model selection and application in functional research on lncRNAs. Thus, we elucidate the concept of deep learning models, explore several prevalent deep learning algorithms and their data preferences, conduct a comprehensive review of recent literature studies with exemplary predictive performance over the past 5 years in conjunction with diverse prediction functions, critically analyze and discuss the merits and limitations of current deep learning models and solutions, while also proposing prospects based on cutting-edge advancements in lncRNA research.